Czech

Not applicable.

The student is able to:
- formulate requirements for filters for noise suppression in ECG signals, EEG, EMG
- design and implement adaptive filters for suppressing power hum in biosignals
- design and implement special filters Lynn type for narrowband interference suppression
- explain the principle of detection of QRS complexes in ECG signals and graphoelements in EEG signals
- describe the principle of detecting the beginning and end of major waves in the ECG signals
- explain the principles of stationarity tests of stochastic signals
- describe the principle of non-parametric and parametric methods for estimating power spectra
- describe the principle of cross-spectra and coherence spectra estimation and their use for analysis of EEG signals
- describe the principle of Poincare maps and their use for signal analysis (HRV, TWA)
- explain the principle of realization mapping for analysis of EEG signals
- explain the principle of continuous estimate the level of surface EMG signal

Students should have knowledge of digital signal processing, be familiar with the ways of describing the linear filters (transfer function, impulse response, difference equations, frequency response). Assuming knowledge of the discrete Fourier transform (DFT) and the ability to interpret the result DFT. The laboratory work is expected knowledge of Matlab programming environment.

Not applicable.

Teaching methods include lectures and computer laboratories. Course is taking advantage of e-learning system. Students have to write a single project/assignment during the course.

- 20 points can be obtained for activity in the laboratory exercises, consisting in elaborating team project
- 80 points can be obtained for the written exam (the written examination is necessary to obtain at least 40 points)

1. Basic classification of biosignals. Genesis of electrical biosignals, action potential and its spread.
2. Electrocardiogram (ECG), its properties and methods of scaning and display. Types of ECG signal processing requirements.
3. Preprocessing of ECG signals, linear and non-linear filters for suppressing interference.
4. Detectors QRS complexes. Delineation of ECG signals, rhythm and morphological analysis.
5. Analysis of heart rate variability (HRV) in the time and frequency domains.
6. Analysis of the T wave alternans (TWA). Analysis of the stress ECG. Phonocardiogram (PKG) and its analysis.
7. Elektrogastrogram (EGG) and its analysis.
8. Electroencephalogram (EEG signal). Analysis of EEG signals in the time domain.
9. Analysis of EEG signals in the frequency domain.
10. Evoked EEG signals, biosignals of visual and auditory systems.
11. Electromyogram (EMG signal), MUAP analysis and analysis of surface EMG signals.
12. Polygraphic biosignals.

Not applicable.

The aim of the course is to familiarize students with the principles of the genesis of various types of biological signals, with their basic properties and methods of digital processing and automated analysis.

Laboratory is compulsory, missed labs must be properly excused and can be replaced after agreement with the teacher.

Not applicable.

Not applicable.

Kozumplík, J.: Analýza biologických signálů. Elektronická skripta FEKT VUT v Brně, Brno, 2013 (CS)
Svatoš, J.: Biologické signály I. Geneze, zpracování a analýza. Skripta FEL ČVUT, Vydavatelství ČVUT, Praha, 1992 (CS)

Not applicable.